Balanced clustering

Balanced clustering is a special case of clustering where, in the strictest sense, cluster sizes are constrained to $\lfloor {n \over k}\rfloor$ or $\lceil {n \over k}\rceil$, where $n$ is the number of points and $k$ is the number of clusters. A typical algorithm is balanced k-means, which minimizes mean square error (MSE). Another type of balanced clustering called balance-driven clustering has a two-objective cost function that minimizes both the imbalance and the MSE. Typical cost functions are ratio cut and Ncut.

Software

There exists implementations for balanced k-means and Ncut